In StereoSR [1]}, Jeon et al. proposed a method that learned a subpixel parallax prior to enhancing the spatial resolution of the stereo images. However, the number of shifted right images is fixed in StereoSR, which makes it fail to handle different stereo images with large disparity variations. To handle this problem, Wang et al. [2]}, [3]} proposed a parallax-attention mechanism with a global receptive field along the epipolar line, which can generate reliable correspondence between the stereo image pair and improve the quality of the reconstructed SR images. In [4]}, a dataset named Flickr1024 is proposed for stereo image super-resolution, which consists of 1024 high-quality stereo image pairs. In [5]}, a stereo attention module is proposed to extend pre-trained SISR networks for stereo image SR, which interacts with stereo information bi-directionally in a symmetric and compact manner. In [6]}, a symmetric bi-directional parallax attention module and an inline occlusion handling scheme are proposed to effectively interact crossview information. In [7]}, a Stereo Super-Resolution and Disparity Estimation Feedback Network (SSRDE-FNet) is proposed to simultaneously handle the stereo image super-resolution and disparity estimation in a unified framework.
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